Issue No. 02 - Feb. (2013 vol. 35)
A. Panagopoulos , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
Chaohui Wang , Center for Visual Comput., Ecole Centrale Paris, Chatenay-Malabry, France
D. Samaras , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
N. Paragios , Center for Visual Comput., Ecole Centrale Paris, Chatenay-Malabry, France
The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.
Geometry, Lighting, Light sources, Three dimensional displays, Estimation, Image edge detection, Solid modeling,image models, Markov random fields, photometry, shading
A. Panagopoulos, Chaohui Wang, D. Samaras, N. Paragios, "Simultaneous Cast Shadows, Illumination and Geometry Inference Using Hypergraphs", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 437-449, Feb. 2013, doi:10.1109/TPAMI.2012.110